Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	54.523898
daal4py_KNeighborsClassifier	0.0	3.0	23.426407
KNeighborsClassifier_kd_tree	0.0	7.0	11.239496
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	48.623110
KMeans	0.0	8.0	31.217493
daal4py_KMeans	0.0	5.0	34.979661
LogisticRegression	0.0	1.0	4.133832
daal4py_LogisticRegression	0.0	0.0	55.390518
Ridge	0.0	0.0	0.995361
daal4py_Ridge	0.0	0.0	0.664602
total	0.0	42.0	25.261941

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.142	0.009	1000000	1000000	100	brute	-1	1	NaN	NaN	0.480	0.001	0.297	0.004	See
1	KNeighborsClassifier	predict	0.166	0.018	1000000	1	100	brute	-1	1	1.0	1.0	0.084	0.001	1.981	0.011	See
2	KNeighborsClassifier	predict	26.292	0.339	1000000	1000	100	brute	-1	1	1.0	1.0	2.030	0.044	12.952	0.001	See
3	KNeighborsClassifier	fit	0.135	0.003	1000000	1000000	100	brute	-1	5	NaN	NaN	0.480	0.003	0.281	0.001	See
4	KNeighborsClassifier	predict	0.163	0.015	1000000	1	100	brute	-1	5	1.0	1.0	0.085	0.003	1.914	0.010	See
5	KNeighborsClassifier	predict	35.994	0.000	1000000	1000	100	brute	-1	5	1.0	1.0	2.013	0.045	17.884	0.000	See
6	KNeighborsClassifier	fit	0.121	0.001	1000000	1000000	100	brute	-1	100	NaN	NaN	0.475	0.002	0.256	0.000	See
7	KNeighborsClassifier	predict	0.167	0.016	1000000	1	100	brute	-1	100	1.0	1.0	0.083	0.001	2.025	0.009	See
8	KNeighborsClassifier	predict	36.445	0.000	1000000	1000	100	brute	-1	100	1.0	1.0	2.071	0.021	17.597	0.000	See
9	KNeighborsClassifier	fit	0.120	0.000	1000000	1000000	100	brute	1	1	NaN	NaN	0.477	0.003	0.252	0.000	See
10	KNeighborsClassifier	predict	0.175	0.002	1000000	1	100	brute	1	1	1.0	1.0	0.083	0.002	2.102	0.000	See
11	KNeighborsClassifier	predict	13.244	0.086	1000000	1000	100	brute	1	1	1.0	1.0	2.016	0.041	6.569	0.000	See
12	KNeighborsClassifier	fit	0.121	0.000	1000000	1000000	100	brute	1	5	NaN	NaN	0.477	0.001	0.254	0.000	See
13	KNeighborsClassifier	predict	0.179	0.002	1000000	1	100	brute	1	5	1.0	1.0	0.081	0.000	2.200	0.000	See
14	KNeighborsClassifier	predict	24.593	0.066	1000000	1000	100	brute	1	5	1.0	1.0	1.979	0.018	12.430	0.000	See
15	KNeighborsClassifier	fit	0.135	0.003	1000000	1000000	100	brute	1	100	NaN	NaN	0.477	0.001	0.283	0.000	See
16	KNeighborsClassifier	predict	0.178	0.002	1000000	1	100	brute	1	100	1.0	1.0	0.086	0.004	2.068	0.003	See
17	KNeighborsClassifier	predict	24.629	0.067	1000000	1000	100	brute	1	100	1.0	1.0	2.052	0.028	12.003	0.000	See
18	KNeighborsClassifier	fit	0.058	0.001	1000000	1000000	2	brute	-1	1	NaN	NaN	0.109	0.004	0.535	0.001	See
19	KNeighborsClassifier	predict	0.019	0.002	1000000	1	2	brute	-1	1	1.0	1.0	0.006	0.000	3.184	0.008	See
20	KNeighborsClassifier	predict	22.271	0.127	1000000	1000	2	brute	-1	1	1.0	1.0	0.309	0.004	72.150	0.000	See
21	KNeighborsClassifier	fit	0.063	0.004	1000000	1000000	2	brute	-1	5	NaN	NaN	0.110	0.004	0.576	0.004	See
22	KNeighborsClassifier	predict	0.025	0.003	1000000	1	2	brute	-1	5	1.0	1.0	0.006	0.001	4.089	0.032	See
23	KNeighborsClassifier	predict	32.896	0.000	1000000	1000	2	brute	-1	5	1.0	1.0	0.315	0.006	104.508	0.000	See
24	KNeighborsClassifier	fit	0.061	0.003	1000000	1000000	2	brute	-1	100	NaN	NaN	0.108	0.002	0.567	0.003	See
25	KNeighborsClassifier	predict	0.024	0.002	1000000	1	2	brute	-1	100	1.0	1.0	0.006	0.001	3.787	0.050	See
26	KNeighborsClassifier	predict	32.215	0.000	1000000	1000	2	brute	-1	100	1.0	1.0	0.363	0.005	88.840	0.000	See
27	KNeighborsClassifier	fit	0.060	0.002	1000000	1000000	2	brute	1	1	NaN	NaN	0.108	0.003	0.553	0.002	See
28	KNeighborsClassifier	predict	0.016	0.001	1000000	1	2	brute	1	1	1.0	1.0	0.006	0.000	2.585	0.008	See
29	KNeighborsClassifier	predict	10.578	0.215	1000000	1000	2	brute	1	1	1.0	1.0	0.306	0.004	34.568	0.001	See
30	KNeighborsClassifier	fit	0.059	0.000	1000000	1000000	2	brute	1	5	NaN	NaN	0.108	0.002	0.547	0.001	See
31	KNeighborsClassifier	predict	0.018	0.001	1000000	1	2	brute	1	5	1.0	1.0	0.006	0.001	2.880	0.015	See
32	KNeighborsClassifier	predict	21.320	0.601	1000000	1000	2	brute	1	5	1.0	1.0	0.313	0.007	68.217	0.001	See
33	KNeighborsClassifier	fit	0.059	0.001	1000000	1000000	2	brute	1	100	NaN	NaN	0.107	0.004	0.553	0.001	See
34	KNeighborsClassifier	predict	0.018	0.001	1000000	1	2	brute	1	100	1.0	1.0	0.006	0.001	2.833	0.008	See
35	KNeighborsClassifier	predict	20.717	0.008	1000000	1000	2	brute	1	100	1.0	1.0	0.367	0.007	56.501	0.000	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	3.104	0.052	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.766	0.020	4.052	0.001	See
1	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.000	0.000	5.530	0.164	See
2	KNeighborsClassifier_kd_tree	predict	0.481	0.007	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.110	0.005	4.391	0.003	See
3	KNeighborsClassifier_kd_tree	fit	3.248	0.136	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	0.806	0.017	4.030	0.002	See
4	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.001	0.000	4.365	0.268	See
5	KNeighborsClassifier_kd_tree	predict	0.841	0.007	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.195	0.008	4.303	0.002	See
6	KNeighborsClassifier_kd_tree	fit	3.235	0.110	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.746	0.010	4.334	0.001	See
7	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.002	0.001	2.726	0.166	See
8	KNeighborsClassifier_kd_tree	predict	2.988	0.057	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.580	0.004	5.153	0.000	See
9	KNeighborsClassifier_kd_tree	fit	3.148	0.029	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.779	0.009	4.040	0.000	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	1.0	1.0	0.000	0.000	1.612	0.359	See
11	KNeighborsClassifier_kd_tree	predict	0.775	0.005	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.104	0.002	7.457	0.000	See
12	KNeighborsClassifier_kd_tree	fit	3.196	0.040	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.752	0.003	4.250	0.000	See
13	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	5	1.0	1.0	0.001	0.000	1.563	0.241	See
14	KNeighborsClassifier_kd_tree	predict	1.473	0.017	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.188	0.002	7.824	0.000	See
15	KNeighborsClassifier_kd_tree	fit	3.115	0.071	1000000	1000000	10	kd_tree	1	100	NaN	NaN	0.781	0.008	3.991	0.001	See
16	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	10	kd_tree	1	100	1.0	1.0	0.002	0.001	1.349	0.140	See
17	KNeighborsClassifier_kd_tree	predict	4.982	0.056	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.580	0.015	8.588	0.001	See
18	KNeighborsClassifier_kd_tree	fit	1.532	0.020	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.522	0.007	2.932	0.000	See
19	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	1	1.0	1.0	0.000	0.000	16.015	0.842	See
20	KNeighborsClassifier_kd_tree	predict	0.031	0.002	1000000	1000	2	kd_tree	-1	1	1.0	1.0	0.001	0.000	32.979	0.169	See
21	KNeighborsClassifier_kd_tree	fit	1.548	0.029	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.542	0.022	2.857	0.002	See
22	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	16.152	0.646	See
23	KNeighborsClassifier_kd_tree	predict	0.033	0.001	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.001	0.001	24.261	0.150	See
24	KNeighborsClassifier_kd_tree	fit	1.465	0.012	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.544	0.013	2.690	0.001	See
25	KNeighborsClassifier_kd_tree	predict	0.002	0.001	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	16.820	0.583	See
26	KNeighborsClassifier_kd_tree	predict	0.051	0.001	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.008	0.002	6.127	0.052	See
27	KNeighborsClassifier_kd_tree	fit	1.500	0.020	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.546	0.008	2.748	0.000	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	1.0	1.0	0.000	0.000	5.094	0.694	See
29	KNeighborsClassifier_kd_tree	predict	0.027	0.000	1000000	1000	2	kd_tree	1	1	1.0	1.0	0.001	0.000	26.679	0.169	See
30	KNeighborsClassifier_kd_tree	fit	1.500	0.013	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.553	0.014	2.713	0.001	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	2.971	0.457	See
32	KNeighborsClassifier_kd_tree	predict	0.030	0.000	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.001	0.000	23.844	0.122	See
33	KNeighborsClassifier_kd_tree	fit	1.507	0.024	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.544	0.009	2.770	0.001	See
34	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	3.655	0.349	See
35	KNeighborsClassifier_kd_tree	predict	0.062	0.000	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.011	0.003	5.745	0.094	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans	fit	0.027	0.029	10000	10000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.007	0.003	4.123	1.437	See
1	KMeans	predict	0.000	0.000	10000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.927	0.492	See
2	KMeans	predict	0.000	0.000	10000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.565	1.061	See
3	KMeans	fit	0.337	0.014	10000	10000	2	full	k-means++	30	300	1	0.0	29.0	NaN	30.0	NaN	0.112	0.003	2.997	0.002	See
4	KMeans	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.982	0.505	See
5	KMeans	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.112	0.102	See
6	KMeans	fit	0.011	0.005	10000	10000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.006	0.001	1.870	0.240	See
7	KMeans	predict	0.000	0.000	10000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.054	0.568	See
8	KMeans	predict	0.000	0.000	10000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.089	0.569	See
9	KMeans	fit	0.135	0.005	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.049	0.002	2.769	0.002	See
10	KMeans	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.126	0.535	See
11	KMeans	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	0.995	0.092	See
12	KMeans	fit	0.157	0.006	10000	10000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.021	0.001	7.596	0.003	See
13	KMeans	predict	0.000	0.000	10000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.798	0.427	See
14	KMeans	predict	0.001	0.000	10000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.010	0.303	See
15	KMeans	fit	0.872	0.033	10000	10000	100	full	k-means++	30	300	1	0.0	24.0	NaN	21.0	NaN	0.406	0.020	2.148	0.004	See
16	KMeans	predict	0.000	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.586	0.281	See
17	KMeans	predict	0.006	0.002	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	4.601	0.093	See
18	KMeans	fit	0.054	0.002	10000	10000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.019	0.001	2.889	0.006	See
19	KMeans	predict	0.000	0.000	10000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.907	0.598	See
20	KMeans	predict	0.001	0.000	10000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.099	0.243	See
21	KMeans	fit	0.271	0.046	10000	10000	100	full	random	30	300	1	0.0	22.0	NaN	24.0	NaN	0.187	0.026	1.449	0.049	See
22	KMeans	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.907	0.311	See
23	KMeans	predict	0.006	0.002	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	4.678	0.160	See
24	KMeans	fit	0.673	0.011	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.486	0.018	1.386	0.002	See
25	KMeans	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.010	0.585	See
26	KMeans	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.823	0.426	See
27	KMeans	fit	30.948	0.000	1000000	1000000	2	full	k-means++	30	300	1	0.0	30.0	NaN	30.0	NaN	9.826	0.065	3.150	0.000	See
28	KMeans	predict	0.000	0.000	1000000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.556	0.582	See
29	KMeans	predict	0.001	0.000	1000000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.239	0.133	See
30	KMeans	fit	0.583	0.010	1000000	1000000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.441	0.022	1.323	0.003	See
31	KMeans	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.959	0.631	See
32	KMeans	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.176	0.619	See
33	KMeans	fit	12.807	0.097	1000000	1000000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	3.417	0.055	3.747	0.000	See
34	KMeans	predict	0.000	0.000	1000000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.470	0.666	See
35	KMeans	predict	0.001	0.000	1000000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.198	0.173	See
36	KMeans	fit	6.642	0.137	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	2.966	0.023	2.240	0.000	See
37	KMeans	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.600	0.416	See
38	KMeans	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.098	0.380	See
39	KMeans	fit	100.163	0.000	1000000	1000000	100	full	k-means++	30	300	1	0.0	30.0	NaN	30.0	NaN	49.694	0.000	2.016	0.000	See
40	KMeans	predict	0.000	0.000	1000000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.552	0.386	See
41	KMeans	predict	0.010	0.002	1000000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	6.698	0.064	See
42	KMeans	fit	5.980	0.058	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	2.825	0.011	2.117	0.000	See
43	KMeans	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.720	0.709	See
44	KMeans	predict	0.001	0.000	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.185	0.353	See
45	KMeans	fit	32.527	0.000	1000000	1000000	100	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	21.906	0.024	1.485	0.000	See
46	KMeans	predict	0.000	0.000	1000000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.808	0.407	See
47	KMeans	predict	0.010	0.001	1000000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	7.132	0.048	See

Plots

Shared hyperparameters:

	value
algorithm	full
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	11.589	0.010	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	11.485	0.121	1.009	0.000	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.381	0.804	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	1.032	0.332	See
3	LogisticRegression	fit	0.790	0.017	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	0.792	0.030	0.997	0.002	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.001	0.001	0.071	1.421	See
5	LogisticRegression	predict	0.002	0.001	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.004	0.001	0.566	0.105	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	0.038	0.002	1000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.022	0.003	1.714	0.016	See
1	Ridge	predict	0.000	0.000	1000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	1.368	1.107	See
2	Ridge	predict	0.000	0.000	1000	100	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.796	0.467	See
3	Ridge	fit	0.008	0.001	10000	10000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.003	0.000	3.020	0.036	See
4	Ridge	predict	0.000	0.000	10000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.628	1.126	See
5	Ridge	predict	0.000	0.000	10000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.868	0.411	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}